Final
Daphne Jacobsberg & Catherine Beck
11/5/2021
SNAP Eligibility per County in the Bay Area
group by county eligibility
We found that Alameda County, Santa Clara, Contra Cost, and San Francisco have the highest number of qualifying households in the Bay Area. Moving on to our equity analysis, we will choose to narrow down to just Alameda County and San Francisco county because of their shared urban density and their differing food health and food access issues which may make them the most interesting to compare.
Equity Analysis of SNAP Eligibility by Race
Compared the totals, the proportion of white people qualifying for SNAP decreased in both counties, the proportion of Black or African American increased in both counties. In San Francisco, the proportion of Asian people qualifying for SNAP increased slightly, whereas in Alameda county it decreased significantly. Some other race alone, native Hawaiian, American Indian and Alaska Native alone, and two or more races increased in both counties. This is not suprising, and the breakdown follows national trends (proportion of white being greatest, then Black/African American, then Hispanic and Asian). Due to our findings, we will be using Black or African American as our focus racial group from now on (health effects only). Though our results would more likely be different if we included ethnicity, for the purpose of this analysis, we will just be concentrating on race.
PUMAs SNAP
##
## Call:
## glm(formula = allocated ~ building + tenure + kitchen + puma,
## family = quasibinomial(), data = bay_pums_factored)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7395 -0.1308 -0.0925 -0.0804 3.8029
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.99476 0.16780 -11.888 < 2e-16 ***
## building1 -0.53764 1.15369 -0.466 0.641208
## building2 -2.38299 1.08377 -2.199 0.027901 *
## building3 -2.39846 1.11907 -2.143 0.032102 *
## building4 -1.52385 1.09828 -1.387 0.165305
## building5 -1.96203 1.11530 -1.759 0.078556 .
## building6 -1.74665 1.11336 -1.569 0.116705
## building7 -2.14812 1.13571 -1.891 0.058577 .
## building8 -1.96011 1.11344 -1.760 0.078349 .
## building9 -1.90332 1.08751 -1.750 0.080104 .
## building10 -16.64537 2442.90475 -0.007 0.994563
## tenure1 -1.94449 0.42379 -4.588 4.49e-06 ***
## tenure2 -1.74122 0.45605 -3.818 0.000135 ***
## tenure3 -1.37416 0.40777 -3.370 0.000753 ***
## tenure4 NA NA NA NA
## kitchen1 0.62022 1.01353 0.612 0.540583
## kitchen2 NA NA NA NA
## puma00102 0.37462 0.25820 1.451 0.146813
## puma00103 0.25043 0.29779 0.841 0.400380
## puma00104 0.83790 0.28209 2.970 0.002978 **
## puma00105 -0.03149 0.33310 -0.095 0.924676
## puma00106 -0.09903 0.33120 -0.299 0.764945
## puma00107 0.75253 0.24647 3.053 0.002267 **
## puma00108 -0.31020 0.40300 -0.770 0.441467
## puma00109 0.02390 0.31199 0.077 0.938939
## puma00110 -0.03119 0.29856 -0.104 0.916798
## puma07501 0.04377 0.28349 0.154 0.877296
## puma07502 -0.29094 0.35408 -0.822 0.411265
## puma07503 -1.03416 0.41854 -2.471 0.013485 *
## puma07504 -14.84549 294.06285 -0.050 0.959737
## puma07505 -1.21286 0.53230 -2.279 0.022704 *
## puma07506 -2.57843 1.01483 -2.541 0.011067 *
## puma07507 0.08109 0.37183 0.218 0.827358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.9956621)
##
## Null deviance: 3008.2 on 25787 degrees of freedom
## Residual deviance: 2448.1 on 25757 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 18Results from logit model: Building Type: No strong correlation between Building Type and SNAP Allocation + Income. Tenure: Although not drastically different, renters are mroe likely to be allocated SNAP than owners. Kitchen: NA? PUMA: While all other PUMAs seem to be fairly unbiased, PUMA 07504 in San Francisco County corresponding to The Mission, Castro, Duboce Triangle and Haight Ashbury, have a strongly negative correlation with SNAP Allocation. Thus, very few people in the area have income below 66000/year and are eligible for SNAP.
CalEnviroScreen: correlating Cardiovascular Health and Poverty in Alameda and San Francisco Counties
This graph shows there is a notable difference in Cardiovascular health between San Francisco and Alameda County. Especially, the San Leandro area in Hayward with a score of 21.04.
In comparison to the previous map, this map shows a much more even distributed distribution of poverty households in each county. There are equally as low or high poverty levels in both areas.
Scatter plot does not show a clear relationship, there are several outliars and the points themselves almost appear to be random.
##
## Call:
## lm(formula = Poverty ~ `Cardiovascular Disease`, data = bay_cardio_poverty_tract)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.261 -10.497 -3.829 6.105 60.538
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.4087 1.8709 7.167 2.49e-12 ***
## `Cardiovascular Disease` 0.8202 0.1667 4.920 1.15e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.74 on 549 degrees of freedom
## Multiple R-squared: 0.04223, Adjusted R-squared: 0.04048
## F-statistic: 24.2 on 1 and 549 DF, p-value: 1.147e-06As you can see, An increase of Cardiovascular Disease in one unit is associated with an increase of Poverty in 13.4; 4.2% of the variation in Cardiovascular Disease is explained by the variation in Poverty. The p-value of 1.147e-06 is <5% making these results statistically significant.
##
## Call:
## lm(formula = log(`Cardiovascular Disease`) ~ Poverty, data = bay_cardio_poverty_tract)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.11942 -0.24454 0.01079 0.22975 0.78557
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1918798 0.0263256 83.261 < 2e-16 ***
## Poverty 0.0047181 0.0009856 4.787 2.18e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3477 on 549 degrees of freedom
## Multiple R-squared: 0.04007, Adjusted R-squared: 0.03832
## F-statistic: 22.92 on 1 and 549 DF, p-value: 2.18e-06
While the results with log are marginally better, they are still not normalized and thus do not allow us to draw any conclusions.
The lowest residuals are concentrated around the San Francisco area while the highest residuals are in Alameda County. This shows that the actual data for San Francisco is similar to the results from our model regression. On the hand, Alameda County, and especially the shoreline communities, have really skewed data meaning our regression results were significantly different from the actual data.
Grocery store landscape
The USDA defines food deserts as both low income areas and ones in which more than a third of the pop at the census tract level lives over a mile from a grocery store or supermarket (10 miles for rural areas). Below is a map of the low income and low access tracts measured at one mile (given all urban areas).